NDVI and a simple model of deciduous forest seasonal dynamics

Birky, Alicia

doi:10.1016/s0304-3800(01)00354-4

Cited by 70 publications

(37 citation statements)

References 11 publications

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“…A known limitation of NDVI index is the decrease or the saturation in sensitivity when leaf area index values (LAI) increase [45,46], such as coffee trees that can reach values of LAI higher than 8 [47]. Delalieux et al [48] found the NDVI saturation effect when leaf area rates of crops exceed the value 5.…”

Section: Discussionmentioning

confidence: 99%

Monitoring biennial bearing effect on coffee yield using modis remote sensing imagery

Bernardes

Moreira

Adami

et al. 2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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Abstract:Coffee is the second most valuable traded commodity worldwide. Brazil is the world's largest coffee producer, responsible for one third of the world production. A coffee plot exhibits high and low production in alternated years, a characteristic so called biennial yield. High yield is generally a result of suitable conditions of foliar biomass. Moreover, in high production years one plot tends to lose more leaves than it does in low production years. In both cases some correlation between coffee yield and leaf biomass can be deduced which can be monitored through time series of vegetation indices derived from satellite imagery. In Brazil, a comprehensive, spatially distributed study assessing this relationship has not yet been done. The objective of this study was to assess possible correlations between coffee yield and MODIS derived vegetation indices in the Brazilian largest coffee-exporting province. We assessed EVI and NDVI MODIS products over the period between 2002 and 2009 in the south of Minas Gerais State whose production accounts for about one third of the Brazilian coffee production. Landsat images were used to obtain a reference map of coffee areas and to identify MODIS 250 m pure pixels overlapping homogeneous coffee crops. Only MODIS pixels with 100% coffee were included in the analysis. A wavelet-based filter was used to smooth EVI and NDVI time profiles. Correlations were observed between variations on yield of coffee plots and variations on vegetation indices for pixels overlapping the same coffee plots. The vegetation index metrics best correlated to yield were the amplitude and the minimum values over the growing season. The best correlations were obtained between variation on OPEN ACCESSRemote Sens. 2012, 4 2493 yield and variation on vegetation indices the previous year (R = 0.74 for minEVI metric and R = 0.68 for minNDVI metric). Although correlations were not enough to estimate coffee yield exclusively from vegetation indices, trends properly reflect the biennial bearing effect on coffee yield.

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Section: Discussionmentioning

confidence: 99%

Monitoring biennial bearing effect on coffee yield using modis remote sensing imagery

Bernardes

Moreira

Adami

et al. 2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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“…Such changes have mainly been analyzed using the Normalized Difference Vegetation Index (NDVI) spectral reflectance index [1,2]. Satellite observations acquired with medium-and coarse-resolution optical sensors have been widely used to monitor seasonal and interannual variations in the amount of vegetation in forest areas [3][4][5]. The low spatial resolution of LAI mapping products is a limiting factor when work is conducted at a local scale, particularly in heterogeneous and sparse forests.…”

Section: Introductionmentioning

confidence: 99%

A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data

et al. 2014

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Airborne laser scanner (ALS) data provide an enhanced capability to remotely map two key variables in forestry: leaf area index (LAI) and tree height (H). Nevertheless, the cost, complexity and accessibility of this technology are not yet suited for meeting the broad demands required for estimating and frequently updating forest data. Here we demonstrate the capability of alternative solutions based on the use of low-cost color infrared (CIR) cameras to estimate tree-level parameters, providing a cost-effective solution for forest inventories. ALS data were acquired with a Leica ALS60 laser scanner and digital aerial imagery (DAI) was acquired with a consumer-grade camera modified for color infrared detection and synchronized with a GPS unit. In this paper we evaluate the generation of a DAI-based canopy height model (CHM) from imagery obtained with low-cost CIR cameras using structure from motion (SfM) and spatial interpolation methods in the context of a complex canopy, as in forestry. Metrics were calculated from the DAI-based CHM and the DAI-based Normalized Difference Vegetation Index (NDVI) for the estimation of tree height and LAI, respectively. Results were compared with the models estimated from ALS provide insight on the appropriateness of using cost-effective 3D photo-reconstruction methods for targeting single trees with irregular and heterogeneous tree crowns in complex open-canopy forests. It quantitatively demonstrates that low-cost CIR cameras can be used to estimate both single-tree height and LAI in forest inventories.

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“…The difference in seasonal NDVI was calculated from 2000 to 2007 and averaged for each WMU (DNDVI ¼ NDVI summer À NDVI winter ) across the study area. NDVI has been demonstrated to covary with primary productivity (Birky 2001), and seasonal differences in NDVI should be proportional to the seasonal greenness of a landscape (i.e., coverage of deciduous foliage; Avgar et al 2013), and also serves to correct the We hypothesized that moose demographic characteristics are governed in part by natural and anthropogenic disturbance regimes. Spatially explicit forest fire and timber harvest records were obtained from the Land Information Ontario data warehouse, and annual fire and harvest were separately calculated for each wildlife management unit as the proportion of the landscape disturbed by each source from 1981 to 2007.…”

Section: Methodsmentioning

confidence: 99%

Characterizing demographic parameters across environmental gradients: a case study with Ontario moose (Alces alces)

et al. 2015

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Abstract. Population-level demographic characteristics as estimated by standard logistic growth models (i.e., carrying capacity and intrinsic growth rate) should vary with changes in habitat quality and availability of resources. However, few published studies have tested this hypothesis by comparing population growth rates across broad bioclimatic gradients, and fewer still the carrying capacities of those populations. We used time series data on moose (Alces alces) population densities based on aerial census and hunter harvest data for 34 management units across Ontario to estimate local carrying capacities and intrinsic growth rates. These population parameters were then regressed against associated habitat covariates for each management unit to assess how moose demography changes across a broad gradient of productivity, habitat abundance, and timber harvest. Moose carrying capacity was found to increase with increasing forest productivity as measured by DNDVI and the proportion of mixedwood stands in the forest. Both variables are plausibly indicative of high quality forage abundance for moose. Moose carrying capacity decreased with the proportion of forest stands harvested for timber annually, suggesting that immediate removal of forest stands and increased access by hunters temper maximum population size. Maximum rates of population growth by Ontario moose did not vary predictably with any of the landscape covariates tested. These findings contribute to our understanding of changes in demography across broad geographic and bioclimatic gradients and suggest that crude population estimators may be derived based on known habitat preferences and resource availability without a priori knowledge of animal abundance.

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NDVI and a simple model of deciduous forest seasonal dynamics

Cited by 70 publications

References 11 publications

Monitoring biennial bearing effect on coffee yield using modis remote sensing imagery

Monitoring biennial bearing effect on coffee yield using modis remote sensing imagery

A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data

Characterizing demographic parameters across environmental gradients: a case study with Ontario moose (Alces alces)

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